In the past, for air-to-fuel (A/F) ratio control of an automobile fuel injection system, feedback control was generally performed by an O.sub.2 sensor or a linear A/F ratio sensor (LAF sensor) as an A/F ratio sensor, and was successful in stationary-state operation (idle-state operation). However, during a transient state in which its speed is accelerating or decelerating, response-delays in the sensor causes the A/F ratio to be controlled with low precision, and thereby a target A/F ratio cannot be achieved. To correct this, depending upon mechanical change such as change of the degree of throttle opening, fuel is subjected to increasing/reducing correction. In this case, however, all the injected fuel does not flow into a cylinder but is deposited on a wall of an intake manifold pipe or an air suction valve, and some of the fuel deposit thereon evaporates and enters the cylinder, which makes it difficult to control the A/F ratio during the transient state in which the speed is accelerating or decelerating the engine is starting.
To pass a ULEV (Ultra Low Emission Vehicle) regulation in the United States of America, it is essential that the A/F ratio be controlled with high precision during the transient state at the starting of the engine, since quantity of HC (hydrocarbon) released during this state occupies about 80% of all in the test mode.
With a view to attaining the above object, a fuel deposit model is constructed and correction quantity of the fuel is found by an inverse system of this model, or as described in Japanese patent publication No. 3-235723, a neural network (NN) is made to learn nonlinearities such as the fuel deposit, to improve response characteristics during the transient state. In the NN, "units" which perform calculations are connected by a weighted "directional link" to construct the same, and the units respectively transmit their outputs through the link to perform information processing. Since the network system stores knowledge (information) in itself and operates adaptively to aims or environments, the A/F ratio could be controlled precisely during the transient state through the use of the network.
The prior art A/F ratio control device is thus constructed. In the internal combustion engine, all of the fuel injected by an injector does not flow into the cylinder but a part of it is deposited on the wall of the intake manifold pipe as described above. The quantity of the fuel deposited thereon varies intricately depending upon operating states (number of engine revolutions or load such as an intake air pressure) or environments (intake air temperature or cooling water temperature, atmospheric pressure, and the like), and the quantity of the evaporated fuel also varies depending upon the operating states or the environments. Hence, if the quantity of the fuel flowing into the cylinder is known, then it becomes possible to control the A/F ratio more precisely particularly during the transient state. However, use of the above deposit model cannot represent such a complicated system and only provides approximation. As a consequence, satisfactory A/F ratio control is not realized.
In a control system using the NN, it is possible to learn complicated behavior. To obtain a generalized estimation value, it is required that the output of the A/F ratio sensor be supplied to the NN as an input. In actuality, however, when the A/F ratio sensor is deactivated at very low temperature or just after the engine starts, it is impossible to perform correction control by the use of the NN which performs calculations on the output value of the sensor as input data, and it is therefore extremely difficult to estimate a generalized and highly precise A/F ratio.